Transmission of data through a communication channel is expedited by compressing the data prior to transmission. Such compression reduces the number of bits required to transmit the data (thereby enhancing the speed of transmission), while the data is recovered or decompressed at the receiving end of the channel with the original number of bits. The efficacy of the compression process is the ratio of the number of bits of the raw data divided by the number of bits of the compressed data, sometimes referred to as the compression ratio.
In most applications, the data is of a single known fixed type, such as video images, scientific data, text, curves, etc. For example, in a communication channel transmitting video data, it is conventional to employ a compression process specifically designed for video images. It is also conventional to adapt or change the parameters of that compression process to optimize it as the image content of the data stream changes over time. Specifically, a compression process for video data can include run length encoding, discrete cosine transforming and quantization. In such a case, adapting the compression parameters can involve changing the quantization parameters for different video frames, depending upon their image content. Such adaptation can be carried out in other compression processes employed in compressing data other than video data.
Different types of information require different types of compression algorithms. For example, a compression algorithm that attains the maximum compression ratio of data representing scientific measurements will not perform as well on a different type of data (e.g., data representing an image). In fact, an ill-suited compression algorithm can be worse than no compression. Therefore, compression can only be attained and optimized using a compression algorithm dedicated to the type of information being compressed.
A typical characteristic of such compression processes is that, while they may be adaptable and responsive to changes in the data, they do not change in type. That is, for example, a video compression process remains a video compression process although it may be adaptive to changes in the image content. Thus, the data processed in such communication channels must be of a particular type (e.g., video data, text, curves, etc.), or else the change in data type must be pre-arranged or known a priori. For example, communication over the internet may involve the transmission of compressed data files containing a particular type of information. The data file itself indicates the decompression process required in opening (“unzipping”) the file or displaying its contents (e.g., video or audio or text), and therefore this information can be discerned after reception at the receiving node in the communication channel. Moreover, in the case of the internet for example, the data compression and other features of the physical layer of the modem remain unchanged and are not adaptive nor responsive to changes in the data or information content in the communication channel. Therefore, in such a communication channel, speed and/or compression is not optimized.
A problem therefore arises in the case of a data channel in which speed is so critical that the compression process consistently must be effective over a wide range of different types of data whose optimum compression algorithms are entirely different are transmitted at different times that vary randomly or are not previously known or indicated. For example, one frame of the data stream may contain text, the next two may contain graphs or statistical data and a further set of frames may contain images, and this pattern may not repeat and may be unknown until the time the data is presented for immediate compression and transmission. Since the effective compression algorithms for the different data types in this example are entirely different, no single compression process or algorithm can suffice. The prior art is therefore inadequate to provide effective compression of each and every frame of such a data stream. This is because typical prior art compression schemes, even those described as being “adaptive”, assume a single type of data being continuously transmitted and therefore require a single type of compression algorithm be used continuously.
This problem arises in real practical situations. For example, missile telemetry involves the high speed transmission of measurement data, statistical data, text and other different types of information. In order to fit more data and information into the telemetry channel, compression of the data must be improved, so that effective compression is achieved for each of the different types of data that are transmitted. The problem is that some of the low frequency information (e.g., scientific measurements) must be compressed using a lossless process, other higher frequency information (performance curves or graphs) can be compressed by curve fitting or statistical characterization and still other information such as images can be compressed using very lossy processes such as those referred to above. Providing effective or nearly optimum compression for the entire data stream is not possible using conventional approaches.